| from typing import * |
| import torch |
| import torch.nn as nn |
| from .. import SparseTensor |
|
|
|
|
| class SparseSpatial2Channel(nn.Module): |
| """ |
| Downsample a sparse tensor by a factor of `factor`. |
| Implemented as rearranging its features from spatial to channel. |
| """ |
| def __init__(self, factor: int = 2): |
| super(SparseSpatial2Channel, self).__init__() |
| self.factor = factor |
|
|
| def forward(self, x: SparseTensor) -> SparseTensor: |
| DIM = x.coords.shape[-1] - 1 |
| cache = x.get_spatial_cache(f'spatial2channel_{self.factor}') |
| if cache is None: |
| coord = list(x.coords.unbind(dim=-1)) |
| for i in range(DIM): |
| coord[i+1] = coord[i+1] // self.factor |
| subidx = x.coords[:, 1:] % self.factor |
| subidx = sum([subidx[..., i] * self.factor ** i for i in range(DIM)]) |
|
|
| MAX = [(s + self.factor - 1) // self.factor for s in x.spatial_shape] |
| OFFSET = torch.cumprod(torch.tensor(MAX[::-1]), 0).tolist()[::-1] + [1] |
| code = sum([c * o for c, o in zip(coord, OFFSET)]) |
| code, idx = code.unique(return_inverse=True) |
|
|
| new_coords = torch.stack( |
| [code // OFFSET[0]] + |
| [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)], |
| dim=-1 |
| ) |
| else: |
| new_coords, idx, subidx = cache |
| |
| new_feats = torch.zeros(new_coords.shape[0] * self.factor ** DIM, x.feats.shape[1], device=x.feats.device, dtype=x.feats.dtype) |
| new_feats[idx * self.factor ** DIM + subidx] = x.feats |
|
|
| out = SparseTensor(new_feats.reshape(new_coords.shape[0], -1), new_coords, None if x._shape is None else torch.Size([x._shape[0], x._shape[1] * self.factor ** DIM])) |
| out._scale = tuple([s * self.factor for s in x._scale]) |
| out._spatial_cache = x._spatial_cache |
| |
| if cache is None: |
| x.register_spatial_cache(f'spatial2channel_{self.factor}', (new_coords, idx, subidx)) |
| out.register_spatial_cache(f'channel2spatial_{self.factor}', (x.coords, idx, subidx)) |
| out.register_spatial_cache(f'shape', torch.Size(MAX)) |
| if self.training: |
| subdivision = torch.zeros((new_coords.shape[0], self.factor ** DIM), device=x.device, dtype=torch.bool) |
| subdivision[idx, subidx] = True |
| out.register_spatial_cache(f'subdivision', subdivision) |
| |
| return out |
|
|
|
|
| class SparseChannel2Spatial(nn.Module): |
| """ |
| Upsample a sparse tensor by a factor of `factor`. |
| Implemented as rearranging its features from channel to spatial. |
| """ |
| def __init__(self, factor: int = 2): |
| super(SparseChannel2Spatial, self).__init__() |
| self.factor = factor |
|
|
| def forward(self, x: SparseTensor, subdivision: Optional[SparseTensor] = None) -> SparseTensor: |
| DIM = x.coords.shape[-1] - 1 |
|
|
| cache = x.get_spatial_cache(f'channel2spatial_{self.factor}') |
| if cache is None: |
| if subdivision is None: |
| raise ValueError('Cache not found. Provide subdivision tensor or pair SparseChannel2Spatial with SparseSpatial2Channel.') |
| else: |
| sub = subdivision.feats |
| N_leaf = sub.sum(dim=-1) |
| subidx = sub.nonzero()[:, -1] |
| new_coords = x.coords.clone().detach() |
| new_coords[:, 1:] *= self.factor |
| new_coords = torch.repeat_interleave(new_coords, N_leaf, dim=0, output_size=subidx.shape[0]) |
| for i in range(DIM): |
| new_coords[:, i+1] += subidx // self.factor ** i % self.factor |
| idx = torch.repeat_interleave(torch.arange(x.coords.shape[0], device=x.device), N_leaf, dim=0, output_size=subidx.shape[0]) |
| else: |
| new_coords, idx, subidx = cache |
|
|
| x_feats = x.feats.reshape(x.feats.shape[0] * self.factor ** DIM, -1) |
| new_feats = x_feats[idx * self.factor ** DIM + subidx] |
| out = SparseTensor(new_feats, new_coords, None if x._shape is None else torch.Size([x._shape[0], x._shape[1] // self.factor ** DIM])) |
| out._scale = tuple([s / self.factor for s in x._scale]) |
| if cache is not None: |
| out._spatial_cache = x._spatial_cache |
| return out |
|
|
